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Comment by yorwba | Hacker News Reader

The paper is interesting because they calculate the theoretically optimal difficulty for a specific class of learning algorithms: https://dx.doi.org/10.1038/s41467-019-12552-4 (I think the method might be applicable for scheduling flashcards better than the rule-of-thumb spacing of Anki et al.)

The Independent article is unfounded speculation about this applying to the way humans learn, without any discussion of whether the model is actually applicable. (Most things humans are trying to learn aren't binary classification tasks.)

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The paper has the Law & Gold model of (monkey) perceptual learning as one of their cases, and has a bit of discussion about how stochastic gradient descent in ML is quantitatively similar to observed characteristics of human learning. I don't think it's unfounded speculation, except perhaps on the part of the paper.

While human learning might be similar, is it similar enough? Humans seem to require very few data points to learn something. Just a few examples are enough for a human to grasp a pattern. As far as I know, we haven't been able to do the same in ML.

I’m not so sure about that. Adult humans can learn new things with a remarkably small number of additional data points, but they’re drawing on a lifetime of human experience which is also data. Children tend to need more help.

Right. We have priors about how the world works, and that usually helps.

ML has training right?

Humans have always thought or related the latest technology to their own functioning, particularly of the brain. It's amazing to see the process of people finding pistons and mechanical automatons as a metaphor for the mind. Then sequential programming and now ML. Humans have an incredible ability to restructure their own ideas about their brain with machines and adapt to new ideas, in a way that would break a machine. Not to mention the rest of human biology that contributes to our functioning minds outside of the brain.

I don't know how to separate out the difference between learning something through immersion and seeing the broader world view that nestles those ideas within them.

I agree the paper is very interesting

Is there a mistake in equation 3? shouldn't it read:

ER = integral (-inf, 0, p(h | ABS( Delta ), sigma ) dh ?

for positive Delta I agree, but with negative delta either the integral should run from 0 to +inf, or the absolute value of Delta should be taken?

Yes, Anki’s goal of “let’s quickly hide all the cards you know and only show you cards you are forgetting or on the edge of forgetting” has never seemed overly useful. The end result is the following algorithm: “let’s present you with the toughest things you can’t remember over and over again, and ignore all the facts you’ve done so well at learning (until you have almost, or completely, forgotten those facts)”

Throwing up cards you can answer in 1 second is not wasting anyone’s time. It is more likely to be encouraging to people to remind people of what they know.

Is there an easy adjustment to the algorithm parameters to make things better?

Wanikani does an amazing job at SRS. I wish Anki followed its model.

I haven’t used either Anki not wanikani (I use SuperMemo) but from what I’ve heard wanikani has no leech management meaning you end up stuck repeating the kanji you keep failing, making the experience miserable. Leech management is really important for any decent SRS

Could you expand on this? I've used both but am not sure what you mean by leech management

Sometimes you'll have much more difficulty with certain items than others. No matter what you do, even after repeated attempts to learn, you just can't get them to stick.

These are "leeches". They burn productive study and review time.

I have a theory that these items have lower adjacency to past experience or knowledge and it's difficult to form mnemonics or other connections. Or they're less novel and don't cause our brain to take interest. That's where all of my leeches lie -- in the realm of things I don't particularly care about.

A good leech management algorithm will back-burner unproductive items so you can focus on the rest of the concept population. There are different types of leeches too -- things you don't get during introduction, or things that you can commit to short term memory but won't stick for long. A good algorithm will identify all of them and block them.

That's really interesting, I'll have to give supermemo a try. I definitely remember those items in WaniKani; a lot of the times they were mnemonics based on pop culture references that I just didn't get, or the mnemonic was just kind of a stretch, or too many similar concepts had been introduced at once. When I stopped I had definitely hit a wall where I just didn't feel like I could keep learning.

Interesting to see the downvote. I guess there are some avid Anki users here.

Leech management is ok, but I think active positive reinforcement is also necessary. Remind the user of how much they know as well.

> The end result is the following algorithm: “let’s present you with the toughest things you can’t remember over and over again, and ignore all the facts you’ve done so well at learning (until you have almost, or completely, forgotten those facts)”

In my understanding, that's closer to optimal. Effortful retrieval is much more effective at strengthening future retrieval.

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